{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kats 203 Time Series Features\n",
    "\n",
    "This tutorial will introduce `TsFeatures` in Kats, which allows you to extract meaningful features from time series.  The table of contents for Kats 203 is as follows:\n",
    "\n",
    "1. Basic Usage with a Single Time Series        \n",
    "2. Applications with Multiple Time Series        \n",
    "    2.1 Largest Seasonal Component        \n",
    "    2.2 Highest Entropy (\"Least Predictable\")        \n",
    "    2.3 Cluster Similar Time Series        \n",
    "3. Out-in/out features for calculation        \n",
    "    3.1 Opting-out features        \n",
    "    3.2 Opting-in features        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** We provide two types of tutorial notebooks\n",
    "- **Kats 101**, basic data structure and functionalities in Kats \n",
    "- **Kats 20x**, advanced topics, including advanced forecasting techniques, advanced detection algorithms, `TsFeatures`, meta-learning, etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "# For Google Colab:\n",
    "!pip install kats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:kats.utils.time_series_parameter_tuning requires ax-platform be installed\n",
      "WARNING:root:kats.models.metalearner.get_metadata requires ax-platform be installed\n",
      "WARNING:root:kats.detectors.prophet_detector is not available (requires Prophet)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pprint\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from kats.consts import TimeSeriesData\n",
    "from statsmodels.tsa.seasonal import STL\n",
    "from kats.utils.simulator import Simulator\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from kats.tsfeatures.tsfeatures import TsFeatures\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purposes of this tutorial, we are going to use the `Simulator` to generate a list of 30 different `TimeSeriesData` objects called `ts_list`.  It contains 10 time series simulated from the ARIMA model, 10 time series with trend shifts, and 10 time series with level shifts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = Simulator(n=90, freq=\"D\", start = \"2021-01-01\") # simulate 90 days of data\n",
    "random_seed = 100\n",
    "\n",
    "# generate 10 TimeSeriesData with arima_sim\n",
    "np.random.seed(random_seed) # setting numpy seed\n",
    "arima_sim_list = [sim.arima_sim(ar=[0.1, 0.05], ma = [0.04, 0.1], d = 1) for _ in range(10)]\n",
    "\n",
    "# generate 10 TimeSeriesData with trend shifts\n",
    "trend_sim_list = [\n",
    "    sim.trend_shift_sim(\n",
    "        cp_arr = [30, 60, 75],\n",
    "        trend_arr=[3, 15, 2, 8],\n",
    "        intercept=30,\n",
    "        noise=50,\n",
    "        seasonal_period=7,\n",
    "        seasonal_magnitude=np.random.uniform(10, 100),\n",
    "        random_seed=random_seed\n",
    "    ) for _ in range(10)\n",
    "]\n",
    "\n",
    "\n",
    "# generate 10 TimeSeriesData with level shifts\n",
    "level_shift_list = [\n",
    "    sim.level_shift_sim(\n",
    "        cp_arr = [30, 60, 75],\n",
    "        level_arr=[1.35, 1.05, 1.35, 1.2],\n",
    "        noise=0.05,\n",
    "        seasonal_period=7,\n",
    "        seasonal_magnitude=np.random.uniform(0.1, 1.0),\n",
    "        random_seed=random_seed\n",
    "    ) for _ in range(10)\n",
    "]\n",
    "\n",
    "ts_list = arima_sim_list + trend_sim_list + level_shift_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Basic Usage with a Single Time Series\n",
    "We begin by introducing the basic usage of `TsFeatures`.  For the purposes of this example, we will use the simulator to simulate a bunch of different time series from the ARIMA model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`TsFeatures` currently can only process one time series a time, so let's start by taking a look at the first time series we generated above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = ts_list[0]\n",
    "\n",
    "# plot the time series\n",
    "ts.plot(cols=['value'])\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extracting the basic features from the time series using `TsFeatures` is straightforward.  We can do so as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Holt-Winters failed Data must be positive.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'length': 90,\n",
       " 'mean': -4.973228083549793,\n",
       " 'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'stability': 45.07760417461487,\n",
       " 'flat_spots': 1,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'std1st_der': 0.8773588739369633,\n",
       " 'crossing_points': 5,\n",
       " 'binarize_mean': 0.43333333333333335,\n",
       " 'unitroot_kpss': 0.4164114707833328,\n",
       " 'heterogeneity': 73.29527168434541,\n",
       " 'histogram_mode': -11.841676172131818,\n",
       " 'linearity': 0.834635526909661,\n",
       " 'trend_strength': 0.9853025999592567,\n",
       " 'seasonality_strength': 0.3521955818150291,\n",
       " 'spikiness': 0.00020455870537077636,\n",
       " 'peak': 1,\n",
       " 'trough': 6,\n",
       " 'level_shift_idx': 23,\n",
       " 'level_shift_size': 0.7134342301151566,\n",
       " 'y_acf1': 0.9597578784708428,\n",
       " 'y_acf5': 4.0361834721280365,\n",
       " 'diff1y_acf1': 0.1830233735938267,\n",
       " 'diff1y_acf5': 0.0794760417768679,\n",
       " 'diff2y_acf1': -0.4816907863327952,\n",
       " 'diff2y_acf5': 0.24476824866108501,\n",
       " 'y_pacf5': 0.9862593061001357,\n",
       " 'diff1y_pacf5': 0.07981792144706332,\n",
       " 'diff2y_pacf5': 0.36145785941160113,\n",
       " 'seas_acf1': 0.8149983814152568,\n",
       " 'seas_pacf1': 0.030344962550473576,\n",
       " 'firstmin_ac': 53,\n",
       " 'firstzero_ac': 30,\n",
       " 'holt_alpha': 0.999999985097681,\n",
       " 'holt_beta': 0.1365684856163694,\n",
       " 'hw_alpha': nan,\n",
       " 'hw_beta': nan,\n",
       " 'hw_gamma': nan}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1. initiate TsFeatures\n",
    "model = TsFeatures()\n",
    "\n",
    "# Step 2. use .transform() method, and apply on the target time series data\n",
    "output_features = model.transform(ts)\n",
    "output_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dictionary above shows 40 features, which are the features that we calculate by default.  There are 28 additional features that we support in `TsFeatures`, and users can select which features they would like to include in their calculations using the `selected_features` argument.  We will see an example of this later.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Applications with Multiple Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will look at each of the time series in `ts_list` and use `TsFeatures` to calculate the features for each of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n",
      "WARNING:root:Holt-Winters failed Data must be positive.\n"
     ]
    }
   ],
   "source": [
    "model = TsFeatures()\n",
    "output_features = [model.transform(ts) for ts in ts_list] # loop through time series data and perform transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can view the results as a `DataFrame` as follows, which has one row for each time series in `ts_list` and each column represents a different feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>entropy</th>\n",
       "      <th>lumpiness</th>\n",
       "      <th>stability</th>\n",
       "      <th>flat_spots</th>\n",
       "      <th>hurst</th>\n",
       "      <th>std1st_der</th>\n",
       "      <th>crossing_points</th>\n",
       "      <th>...</th>\n",
       "      <th>diff2y_pacf5</th>\n",
       "      <th>seas_acf1</th>\n",
       "      <th>seas_pacf1</th>\n",
       "      <th>firstmin_ac</th>\n",
       "      <th>firstzero_ac</th>\n",
       "      <th>holt_alpha</th>\n",
       "      <th>holt_beta</th>\n",
       "      <th>hw_alpha</th>\n",
       "      <th>hw_beta</th>\n",
       "      <th>hw_gamma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>-4.973228</td>\n",
       "      <td>50.694998</td>\n",
       "      <td>0.274245</td>\n",
       "      <td>10.258210</td>\n",
       "      <td>45.077604</td>\n",
       "      <td>1</td>\n",
       "      <td>0.418844</td>\n",
       "      <td>0.877359</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.361458</td>\n",
       "      <td>0.814998</td>\n",
       "      <td>0.030345</td>\n",
       "      <td>53</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.365685e-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90</td>\n",
       "      <td>-2.058767</td>\n",
       "      <td>10.041589</td>\n",
       "      <td>0.366121</td>\n",
       "      <td>23.888037</td>\n",
       "      <td>5.711696</td>\n",
       "      <td>1</td>\n",
       "      <td>0.449241</td>\n",
       "      <td>0.701685</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.749517</td>\n",
       "      <td>0.327535</td>\n",
       "      <td>-0.226641</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>0.866485</td>\n",
       "      <td>9.109527e-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90</td>\n",
       "      <td>9.908474</td>\n",
       "      <td>54.513387</td>\n",
       "      <td>0.371858</td>\n",
       "      <td>44.741126</td>\n",
       "      <td>45.977900</td>\n",
       "      <td>1</td>\n",
       "      <td>0.249448</td>\n",
       "      <td>0.983165</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.324193</td>\n",
       "      <td>0.668572</td>\n",
       "      <td>0.124300</td>\n",
       "      <td>55</td>\n",
       "      <td>31</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.547459e-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90</td>\n",
       "      <td>-4.852956</td>\n",
       "      <td>21.728208</td>\n",
       "      <td>0.492625</td>\n",
       "      <td>9.067492</td>\n",
       "      <td>16.945737</td>\n",
       "      <td>1</td>\n",
       "      <td>0.377345</td>\n",
       "      <td>0.705047</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0.533415</td>\n",
       "      <td>0.524764</td>\n",
       "      <td>0.009480</td>\n",
       "      <td>31</td>\n",
       "      <td>27</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.692558e-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>-1.521848</td>\n",
       "      <td>28.601801</td>\n",
       "      <td>0.402300</td>\n",
       "      <td>259.161089</td>\n",
       "      <td>17.962647</td>\n",
       "      <td>1</td>\n",
       "      <td>0.543165</td>\n",
       "      <td>0.880529</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.460363</td>\n",
       "      <td>0.398363</td>\n",
       "      <td>-0.106916</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.024176e-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   length      mean        var   entropy   lumpiness  stability  flat_spots  \\\n",
       "0      90 -4.973228  50.694998  0.274245   10.258210  45.077604           1   \n",
       "1      90 -2.058767  10.041589  0.366121   23.888037   5.711696           1   \n",
       "2      90  9.908474  54.513387  0.371858   44.741126  45.977900           1   \n",
       "3      90 -4.852956  21.728208  0.492625    9.067492  16.945737           1   \n",
       "4      90 -1.521848  28.601801  0.402300  259.161089  17.962647           1   \n",
       "\n",
       "      hurst  std1st_der  crossing_points  ...  diff2y_pacf5  seas_acf1  \\\n",
       "0  0.418844    0.877359                5  ...      0.361458   0.814998   \n",
       "1  0.449241    0.701685                8  ...      0.749517   0.327535   \n",
       "2  0.249448    0.983165                3  ...      0.324193   0.668572   \n",
       "3  0.377345    0.705047               11  ...      0.533415   0.524764   \n",
       "4  0.543165    0.880529                6  ...      0.460363   0.398363   \n",
       "\n",
       "   seas_pacf1  firstmin_ac  firstzero_ac  holt_alpha     holt_beta  hw_alpha  \\\n",
       "0    0.030345           53            30    1.000000  1.365685e-01       NaN   \n",
       "1   -0.226641           21            11    0.866485  9.109527e-02       NaN   \n",
       "2    0.124300           55            31    1.000000  4.547459e-10       NaN   \n",
       "3    0.009480           31            27    1.000000  6.692558e-11       NaN   \n",
       "4   -0.106916           25            16    1.000000  1.024176e-01       NaN   \n",
       "\n",
       "   hw_beta  hw_gamma  \n",
       "0      NaN       NaN  \n",
       "1      NaN       NaN  \n",
       "2      NaN       NaN  \n",
       "3      NaN       NaN  \n",
       "4      NaN       NaN  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features = pd.DataFrame(output_features) # converting to dataframe\n",
    "df_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will take a look at some of the applications of the features we have just calculated for each each of the time series in `TsFeatures`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Largest Seasonal Component\n",
    "We will demonstrate leveraging `TsFeatures` for finding the time series data with the highest seasonality strength among the simulated time series data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# finding the index of the time series sample with the highest seasonality strength\n",
    "index_target_ts = df_features['seasonality_strength'].argmax() \n",
    "\n",
    "target_ts = ts_list[index_target_ts] \n",
    "\n",
    "# Plot the time series\n",
    "target_ts.plot(cols=['value'])\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's take a look at the STL decomposition of this time series to see its seasonal component.  We use the [`STL` module from `statsmodels`](https://www.statsmodels.org/devel/generated/statsmodels.tsa.seasonal.STL.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stl = STL(target_ts.value.values, period=7)\n",
    "res = stl.fit()\n",
    "plt.plot(\n",
    "    pd.to_datetime(target_ts.time.values),\n",
    "    res.seasonal\n",
    ")\n",
    "plt.xticks(rotation = 90);\n",
    "plt.title(f'Seasonal component - variance: {np.round(np.var(res.seasonal), 2)}');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we repeat the process for the time series in `ts_list` with the smallest seasonal component.  We see that the variance of the seasonal components is much smaller in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# finding the index of the time series sample with the smallest seasonality strength\n",
    "index_target_ts = df_features['seasonality_strength'].argmin() \n",
    "target_ts = ts_list[index_target_ts].to_dataframe() \n",
    "\n",
    "# Do an STL decomposition and plot the results\n",
    "stl = STL(target_ts.value.values, period=7)\n",
    "res = stl.fit()\n",
    "plt.plot(\n",
    "    pd.to_datetime(target_ts.time.values),\n",
    "    res.seasonal\n",
    ")\n",
    "plt.xticks(rotation = 45);\n",
    "plt.title(f'Seasonal component - variance: {np.round(np.var(res.seasonal), 2)}');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Highest Entropy (\"Least Predictable\")\n",
    "\n",
    "We can intuitively understand entropy as the measure of the disorder of a time series.  In general, a time series with higher entropy will be harder to forecast.  Here, we show how to use `TsFeatures` to find the time series in `ts_list` with the highest entropy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the index of the time series sample with the highest entropy\n",
    "index_target_ts = df_features['entropy'].argmax() \n",
    "\n",
    "target_ts = ts_list[index_target_ts] \n",
    "\n",
    "# Plot the time series\n",
    "target_ts.plot(cols=['value'])\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the above figure with the time series data with the lowest entropy identified by `TsFeatures`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the index of the time series sample with the lowest entropy\n",
    "index_target_ts = df_features['entropy'].argmin() \n",
    "target_ts = ts_list[index_target_ts]\n",
    "\n",
    "# Plot the time series\n",
    "target_ts.plot(cols=['value'])\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the figures above, this second plot shows a time series with a clear change point and two distinct trends, suggesting it is easier to forecast than the first time series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Cluster Similar Time Series\n",
    "\n",
    "Here we are going to use the features we get from `TsFeatures` to try to cluster the time series.  \n",
    "\n",
    "Let's perform a dimension reduction on the simulated time series data, and visualize to see if there's clear pattern of clusters.  In this example, we pick 5 features to use for each time series, and then we use `PCA` (combined with `StandardScaler`) from `sklearn` to project this representation into two dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# performing dimension reduction on the time series samples\n",
    "ls_features = ['lumpiness', 'entropy', 'seasonality_strength', 'stability', 'level_shift_size']\n",
    "df_dataset = df_features[ls_features]\n",
    "x_2d = PCA(n_components=2).fit_transform(X=StandardScaler().fit_transform(df_dataset[ls_features].values))\n",
    "df_dataset['pca_component_1'] = x_2d[:,0]\n",
    "df_dataset['pca_component_2'] = x_2d[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot the results below.  We have color-coded the different types of simulated time series (ARIMA, trend shift, level shift) and we can see that series of the same type are closer to each other.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the PCA projections of each time series\n",
    "plt.figure(figsize = (15,8))\n",
    "# Plot ARIMA time series in red\n",
    "ax = df_dataset.iloc[0:10].plot(x='pca_component_1', y='pca_component_2', kind='scatter', color='red')\n",
    "# Plot trend shift time series in green\n",
    "df_dataset.iloc[10:20].plot(x='pca_component_1', y='pca_component_2', kind='scatter', color='green', ax=ax)\n",
    "# Plot level shift time series in yellow\n",
    "df_dataset.iloc[20:].plot(x='pca_component_1', y='pca_component_2', kind='scatter', color='yellow', ax=ax)\n",
    "\n",
    "plt.title('Visualization of the dimension reduced time series samples')\n",
    "plt.legend(['ARIMA', 'Trend Shift', 'Level Shift'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Out-in/out features for calculation\n",
    "\n",
    "In `TsFeatures`, you can choose which features and feature groups you would like to calculate and which you would like to skip.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Opting-out features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by returning to our initial example, where we calculated the first time series in `ts_list`.  This calculates the 40 features that we calculate by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Holt-Winters failed Data must be positive.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'length': 90,\n",
       " 'mean': -4.973228083549793,\n",
       " 'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'stability': 45.07760417461487,\n",
       " 'flat_spots': 1,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'std1st_der': 0.8773588739369633,\n",
       " 'crossing_points': 5,\n",
       " 'binarize_mean': 0.43333333333333335,\n",
       " 'unitroot_kpss': 0.4164114707833328,\n",
       " 'heterogeneity': 73.29527168434541,\n",
       " 'histogram_mode': -11.841676172131818,\n",
       " 'linearity': 0.834635526909661,\n",
       " 'trend_strength': 0.9853025999592567,\n",
       " 'seasonality_strength': 0.3521955818150291,\n",
       " 'spikiness': 0.00020455870537077636,\n",
       " 'peak': 1,\n",
       " 'trough': 6,\n",
       " 'level_shift_idx': 23,\n",
       " 'level_shift_size': 0.7134342301151566,\n",
       " 'y_acf1': 0.9597578784708428,\n",
       " 'y_acf5': 4.0361834721280365,\n",
       " 'diff1y_acf1': 0.1830233735938267,\n",
       " 'diff1y_acf5': 0.0794760417768679,\n",
       " 'diff2y_acf1': -0.4816907863327952,\n",
       " 'diff2y_acf5': 0.24476824866108501,\n",
       " 'y_pacf5': 0.9862593061001357,\n",
       " 'diff1y_pacf5': 0.07981792144706332,\n",
       " 'diff2y_pacf5': 0.36145785941160113,\n",
       " 'seas_acf1': 0.8149983814152568,\n",
       " 'seas_pacf1': 0.030344962550473576,\n",
       " 'firstmin_ac': 53,\n",
       " 'firstzero_ac': 30,\n",
       " 'holt_alpha': 0.999999985097681,\n",
       " 'holt_beta': 0.1365684856163694,\n",
       " 'hw_alpha': nan,\n",
       " 'hw_beta': nan,\n",
       " 'hw_gamma': nan}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = ts_list[0]\n",
    "TsFeatures().transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can omit a single feature, like `seasonality_strength`, by passing `seasonality_strength=False` into `TsFeatures` like such."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Holt-Winters failed Data must be positive.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'length': 90,\n",
       " 'mean': -4.973228083549793,\n",
       " 'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'stability': 45.07760417461487,\n",
       " 'flat_spots': 1,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'std1st_der': 0.8773588739369633,\n",
       " 'crossing_points': 5,\n",
       " 'binarize_mean': 0.43333333333333335,\n",
       " 'unitroot_kpss': 0.4164114707833328,\n",
       " 'heterogeneity': 73.29527168434541,\n",
       " 'histogram_mode': -11.841676172131818,\n",
       " 'linearity': 0.834635526909661,\n",
       " 'trend_strength': 0.9853025999592567,\n",
       " 'spikiness': 0.00020455870537077636,\n",
       " 'peak': 1,\n",
       " 'trough': 6,\n",
       " 'level_shift_idx': 23,\n",
       " 'level_shift_size': 0.7134342301151566,\n",
       " 'y_acf1': 0.9597578784708428,\n",
       " 'y_acf5': 4.0361834721280365,\n",
       " 'diff1y_acf1': 0.1830233735938267,\n",
       " 'diff1y_acf5': 0.0794760417768679,\n",
       " 'diff2y_acf1': -0.4816907863327952,\n",
       " 'diff2y_acf5': 0.24476824866108501,\n",
       " 'y_pacf5': 0.9862593061001357,\n",
       " 'diff1y_pacf5': 0.07981792144706332,\n",
       " 'diff2y_pacf5': 0.36145785941160113,\n",
       " 'seas_acf1': 0.8149983814152568,\n",
       " 'seas_pacf1': 0.030344962550473576,\n",
       " 'firstmin_ac': 53,\n",
       " 'firstzero_ac': 30,\n",
       " 'holt_alpha': 0.999999985097681,\n",
       " 'holt_beta': 0.1365684856163694,\n",
       " 'hw_alpha': nan,\n",
       " 'hw_beta': nan,\n",
       " 'hw_gamma': nan}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TsFeatures(seasonality_strength=False).transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `seasonality_strength` feature belongs to the 'stl_features' feature group.  Each of the 68 features that we currently support in `TsFeatures` are arranged into 14 feature groups.  Below is the dictionary displaying the mapping feature feature groups and features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acfpacf_features': ['y_acf1',\n",
      "                      'y_acf5',\n",
      "                      'diff1y_acf1',\n",
      "                      'diff1y_acf5',\n",
      "                      'diff2y_acf1',\n",
      "                      'diff2y_acf5',\n",
      "                      'y_pacf5',\n",
      "                      'diff1y_pacf5',\n",
      "                      'diff2y_pacf5',\n",
      "                      'seas_acf1',\n",
      "                      'seas_pacf1'],\n",
      " 'bocp_detector': ['bocp_num', 'bocp_conf_max', 'bocp_conf_mean'],\n",
      " 'cusum_detector': ['cusum_num',\n",
      "                    'cusum_conf',\n",
      "                    'cusum_cp_index',\n",
      "                    'cusum_delta',\n",
      "                    'cusum_llr',\n",
      "                    'cusum_regression_detected',\n",
      "                    'cusum_stable_changepoint',\n",
      "                    'cusum_p_value'],\n",
      " 'holt_params': ['holt_alpha', 'holt_beta'],\n",
      " 'hw_params': ['hw_alpha', 'hw_beta', 'hw_gamma'],\n",
      " 'level_shift_features': ['level_shift_idx', 'level_shift_size'],\n",
      " 'nowcasting': ['nowcast_roc',\n",
      "                'nowcast_ma',\n",
      "                'nowcast_mom',\n",
      "                'nowcast_lag',\n",
      "                'nowcast_macd',\n",
      "                'nowcast_macdsign',\n",
      "                'nowcast_macddiff'],\n",
      " 'outlier_detector': ['outlier_num'],\n",
      " 'robust_stat_detector': ['robust_num', 'robust_metric_mean'],\n",
      " 'seasonalities': ['seasonal_period',\n",
      "                   'trend_mag',\n",
      "                   'seasonality_mag',\n",
      "                   'residual_std'],\n",
      " 'special_ac': ['firstmin_ac', 'firstzero_ac'],\n",
      " 'statistics': ['length',\n",
      "                'mean',\n",
      "                'var',\n",
      "                'entropy',\n",
      "                'lumpiness',\n",
      "                'stability',\n",
      "                'flat_spots',\n",
      "                'hurst',\n",
      "                'std1st_der',\n",
      "                'crossing_points',\n",
      "                'binarize_mean',\n",
      "                'unitroot_kpss',\n",
      "                'heterogeneity',\n",
      "                'histogram_mode',\n",
      "                'linearity'],\n",
      " 'stl_features': ['trend_strength',\n",
      "                  'seasonality_strength',\n",
      "                  'spikiness',\n",
      "                  'peak',\n",
      "                  'trough'],\n",
      " 'time': ['time_years',\n",
      "          'time_months',\n",
      "          'time_monthsofyear',\n",
      "          'time_weeks',\n",
      "          'time_weeksofyear',\n",
      "          'time_days',\n",
      "          'time_daysofyear',\n",
      "          'time_avg_timezone_offset',\n",
      "          'time_length_days',\n",
      "          'time_freq_Monday',\n",
      "          'time_freq_Tuesday',\n",
      "          'time_freq_Wednesday',\n",
      "          'time_freq_Thursday',\n",
      "          'time_freq_Friday',\n",
      "          'time_freq_Saturday',\n",
      "          'time_freq_Sunday'],\n",
      " 'trend_detector': ['trend_num', 'trend_num_increasing', 'trend_avg_abs_tau']}\n"
     ]
    }
   ],
   "source": [
    "feature_group_mapping = TsFeatures().feature_group_mapping\n",
    "pprint.pprint(feature_group_mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can opt to skip the calculation of an entire feature group like `stl_features` the same way we skipped the calculation of `seasonality_strength` in the example above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Holt-Winters failed Data must be positive.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'length': 90,\n",
       " 'mean': -4.973228083549793,\n",
       " 'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'stability': 45.07760417461487,\n",
       " 'flat_spots': 1,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'std1st_der': 0.8773588739369633,\n",
       " 'crossing_points': 5,\n",
       " 'binarize_mean': 0.43333333333333335,\n",
       " 'unitroot_kpss': 0.4164114707833328,\n",
       " 'heterogeneity': 73.29527168434541,\n",
       " 'histogram_mode': -11.841676172131818,\n",
       " 'linearity': 0.834635526909661,\n",
       " 'level_shift_idx': 23,\n",
       " 'level_shift_size': 0.7134342301151566,\n",
       " 'y_acf1': 0.9597578784708428,\n",
       " 'y_acf5': 4.0361834721280365,\n",
       " 'diff1y_acf1': 0.1830233735938267,\n",
       " 'diff1y_acf5': 0.0794760417768679,\n",
       " 'diff2y_acf1': -0.4816907863327952,\n",
       " 'diff2y_acf5': 0.24476824866108501,\n",
       " 'y_pacf5': 0.9862593061001357,\n",
       " 'diff1y_pacf5': 0.07981792144706332,\n",
       " 'diff2y_pacf5': 0.36145785941160113,\n",
       " 'seas_acf1': 0.8149983814152568,\n",
       " 'seas_pacf1': 0.030344962550473576,\n",
       " 'firstmin_ac': 53,\n",
       " 'firstzero_ac': 30,\n",
       " 'holt_alpha': 0.999999985097681,\n",
       " 'holt_beta': 0.1365684856163694,\n",
       " 'hw_alpha': nan,\n",
       " 'hw_beta': nan,\n",
       " 'hw_gamma': nan}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TsFeatures(stl_features=False).transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also opt-out combinations of features and feature groups like su"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Holt-Winters failed Data must be positive.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'stability': 45.07760417461487,\n",
       " 'flat_spots': 1,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'std1st_der': 0.8773588739369633,\n",
       " 'crossing_points': 5,\n",
       " 'binarize_mean': 0.43333333333333335,\n",
       " 'unitroot_kpss': 0.4164114707833328,\n",
       " 'heterogeneity': 73.29527168434541,\n",
       " 'histogram_mode': -11.841676172131818,\n",
       " 'linearity': 0.834635526909661,\n",
       " 'level_shift_idx': 23,\n",
       " 'level_shift_size': 0.7134342301151566,\n",
       " 'y_acf1': 0.9597578784708428,\n",
       " 'y_acf5': 4.0361834721280365,\n",
       " 'diff1y_acf1': 0.1830233735938267,\n",
       " 'diff1y_acf5': 0.0794760417768679,\n",
       " 'diff2y_acf1': -0.4816907863327952,\n",
       " 'diff2y_acf5': 0.24476824866108501,\n",
       " 'y_pacf5': 0.9862593061001357,\n",
       " 'diff1y_pacf5': 0.07981792144706332,\n",
       " 'diff2y_pacf5': 0.36145785941160113,\n",
       " 'seas_acf1': 0.8149983814152568,\n",
       " 'seas_pacf1': 0.030344962550473576,\n",
       " 'firstmin_ac': 53,\n",
       " 'firstzero_ac': 30,\n",
       " 'holt_alpha': 0.999999985097681,\n",
       " 'holt_beta': 0.1365684856163694,\n",
       " 'hw_alpha': nan,\n",
       " 'hw_beta': nan,\n",
       " 'hw_gamma': nan}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsf = TsFeatures(length = False, mean = False, stl_features=False)\n",
    "tsf.transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Opting-in features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `selected_features` argument in `TsFeatures` to specify which features and feature groups we would like to include in our calculations.  When we use this argument, a default feature will not be included unless that feature or its group is explicited included in `selected_features`.  Similarly, a feature not included by default can be included by including it or its group in `selected_features`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example where we specify a list of features to calculate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean': -4.973228083549793,\n",
       " 'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'trend_strength': 0.9853025999592567,\n",
       " 'y_acf1': 0.9597578784708428,\n",
       " 'firstmin_ac': 53,\n",
       " 'holt_alpha': 0.999999985097681,\n",
       " 'bocp_num': 2,\n",
       " 'nowcast_roc': 0.05212172487338159,\n",
       " 'seasonality_mag': 1.0}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TsFeatures(selected_features = [\n",
    "    'mean',\n",
    "    'var',\n",
    "    'entropy',\n",
    "    'lumpiness',\n",
    "    'hurst',\n",
    "    'trend_strength',\n",
    "    'y_acf1',\n",
    "    'firstmin_ac',\n",
    "    'holt_alpha',\n",
    "    'nowcast_roc',\n",
    "    'bocp_num',\n",
    "    'seasonality_mag',\n",
    "]).transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example where we specify a list of features groups to calculate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'length': 90,\n",
       " 'mean': -4.973228083549793,\n",
       " 'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'stability': 45.07760417461487,\n",
       " 'flat_spots': 1,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'std1st_der': 0.8773588739369633,\n",
       " 'crossing_points': 5,\n",
       " 'binarize_mean': 0.43333333333333335,\n",
       " 'unitroot_kpss': 0.4164114707833328,\n",
       " 'heterogeneity': 73.29527168434541,\n",
       " 'histogram_mode': -11.841676172131818,\n",
       " 'linearity': 0.834635526909661,\n",
       " 'y_acf1': 0.9597578784708428,\n",
       " 'y_acf5': 4.0361834721280365,\n",
       " 'diff1y_acf1': 0.1830233735938267,\n",
       " 'diff1y_acf5': 0.0794760417768679,\n",
       " 'diff2y_acf1': -0.4816907863327952,\n",
       " 'diff2y_acf5': 0.24476824866108501,\n",
       " 'y_pacf5': 0.9862593061001357,\n",
       " 'diff1y_pacf5': 0.07981792144706332,\n",
       " 'diff2y_pacf5': 0.36145785941160113,\n",
       " 'seas_acf1': 0.8149983814152568,\n",
       " 'seas_pacf1': 0.030344962550473576,\n",
       " 'nowcast_roc': 0.05212172487338159,\n",
       " 'nowcast_mom': -0.9575428298227386,\n",
       " 'nowcast_ma': -4.832882514728335,\n",
       " 'nowcast_lag': -4.393327628568679,\n",
       " 'nowcast_macd': -0.9380868822690636,\n",
       " 'nowcast_macdsign': -1.0413322101367146,\n",
       " 'nowcast_macddiff': -0.049750365563814056,\n",
       " 'time_years': 1,\n",
       " 'time_months': 3,\n",
       " 'time_monthsofyear': 3,\n",
       " 'time_weeks': 14,\n",
       " 'time_weeksofyear': 14,\n",
       " 'time_days': 31,\n",
       " 'time_daysofyear': 90,\n",
       " 'time_avg_timezone_offset': 0.0,\n",
       " 'time_length_days': 89,\n",
       " 'time_freq_Monday': 0.14444444444444443,\n",
       " 'time_freq_Tuesday': 0.14444444444444443,\n",
       " 'time_freq_Wednesday': 0.14444444444444443,\n",
       " 'time_freq_Thursday': 0.13333333333333333,\n",
       " 'time_freq_Friday': 0.14444444444444443,\n",
       " 'time_freq_Saturday': 0.14444444444444443,\n",
       " 'time_freq_Sunday': 0.14444444444444443}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TsFeatures(selected_features = [\n",
    "    'statistics',\n",
    "    'acfpacf_features',\n",
    "    'nowcasting',\n",
    "    'time',\n",
    "]).transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can specify a combination of features and feature groups to calculate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'length': 90,\n",
       " 'mean': -4.973228083549793,\n",
       " 'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'stability': 45.07760417461487,\n",
       " 'flat_spots': 1,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'std1st_der': 0.8773588739369633,\n",
       " 'crossing_points': 5,\n",
       " 'binarize_mean': 0.43333333333333335,\n",
       " 'unitroot_kpss': 0.4164114707833328,\n",
       " 'heterogeneity': 73.29527168434541,\n",
       " 'histogram_mode': -11.841676172131818,\n",
       " 'linearity': 0.834635526909661,\n",
       " 'seasonality_strength': 0.3521955818150291,\n",
       " 'y_acf1': 0.9597578784708428,\n",
       " 'y_acf5': 4.0361834721280365,\n",
       " 'diff1y_acf1': 0.1830233735938267,\n",
       " 'diff1y_acf5': 0.0794760417768679,\n",
       " 'diff2y_acf1': -0.4816907863327952,\n",
       " 'diff2y_acf5': 0.24476824866108501,\n",
       " 'y_pacf5': 0.9862593061001357,\n",
       " 'diff1y_pacf5': 0.07981792144706332,\n",
       " 'diff2y_pacf5': 0.36145785941160113,\n",
       " 'seas_acf1': 0.8149983814152568,\n",
       " 'seas_pacf1': 0.030344962550473576,\n",
       " 'nowcast_roc': 0.05212172487338159,\n",
       " 'nowcast_mom': -0.9575428298227386,\n",
       " 'nowcast_ma': -4.832882514728335,\n",
       " 'nowcast_lag': -4.393327628568679,\n",
       " 'nowcast_macd': -0.9380868822690636,\n",
       " 'nowcast_macdsign': -1.0413322101367146,\n",
       " 'nowcast_macddiff': -0.049750365563814056}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TsFeatures(selected_features = [\n",
    "    'statistics',\n",
    "    'acfpacf_features',\n",
    "    'nowcasting',\n",
    "    'seasonality_strength',\n",
    "]).transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we can mix up the opt-in and opt-out to calculate majority of the features among some feature groups, while opting-out some of the features within these feature groups that are opt-in.\n",
    "\n",
    "We can also opt-out specific features from the feature groups we include in `selected_features`.  Here is an example where we calculate all of the features in the `statistics` group except for the `mean`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'length': 90,\n",
       " 'var': 50.69499812650379,\n",
       " 'entropy': 0.2742447620827895,\n",
       " 'lumpiness': 10.258210327109449,\n",
       " 'stability': 45.07760417461487,\n",
       " 'flat_spots': 1,\n",
       " 'hurst': 0.41884368965647256,\n",
       " 'std1st_der': 0.8773588739369633,\n",
       " 'crossing_points': 5,\n",
       " 'binarize_mean': 0.43333333333333335,\n",
       " 'unitroot_kpss': 0.4164114707833328,\n",
       " 'heterogeneity': 73.29527168434541,\n",
       " 'histogram_mode': -11.841676172131818,\n",
       " 'linearity': 0.834635526909661}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TsFeatures(selected_features = ['statistics'], mean = False).transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we include the name of a specific feature in `selected_features` and then try to opt-out that same feature or its feature group, we will get an error.  For example, the inverse of our previous example doesn't work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# THIS DOES NOT WORK\n",
    "# TsFeatures(selected_features = ['mean'], statitics = False).transform(ts)\n",
    "\n",
    "# THIS ALSO DOES NOT WORK\n",
    "# TsFeatures(selected_features = ['mean'], mean = False).transform(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In this tutorial, we've demonstrated basic functions of `TsFeatures`, along with the demonstration of some of the interesting applications. We hope you've enjoyed the tutorial and look forward to using `TsFeatures` in the future!"
   ]
  }
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